{"title"=>"Predicting non return to work after orthopaedic trauma: The Wallis Occupational Rehabilitation RisK (WORRK) model", "type"=>"journal", "authors"=>[{"first_name"=>"François", "last_name"=>"Luthi", "scopus_author_id"=>"35332573000"}, {"first_name"=>"Olivier", "last_name"=>"Deriaz", "scopus_author_id"=>"6701613761"}, {"first_name"=>"Philippe", "last_name"=>"Vuistiner", "scopus_author_id"=>"55210816900"}, {"first_name"=>"Cyrille", "last_name"=>"Burrus", "scopus_author_id"=>"35085830300"}, {"first_name"=>"Roger", "last_name"=>"Hilfiker", "scopus_author_id"=>"26648630400"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"372971479", "sgr"=>"84899503059", "pmid"=>"24718689", "scopus"=>"2-s2.0-84899503059", "doi"=>"10.1371/journal.pone.0094268", "issn"=>"19326203"}, "id"=>"308b989e-6a88-37b3-a5c9-167163854e9a", "abstract"=>"BACKGROUND: Workers with persistent disabilities after orthopaedic trauma may need occupational rehabilitation. Despite various risk profiles for non-return-to-work (non-RTW), there is no available predictive model. Moreover, injured workers may have various origins (immigrant workers), which may either affect their return to work or their eligibility for research purposes. The aim of this study was to develop and validate a predictive model that estimates the likelihood of non-RTW after occupational rehabilitation using predictors which do not rely on the worker's background. METHODS: Prospective cohort study (3177 participants, native (51%) and immigrant workers (49%)) with two samples: a) Development sample with patients from 2004 to 2007 with Full and Reduced Models, b) External validation of the Reduced Model with patients from 2008 to March 2010. We collected patients' data and biopsychosocial complexity with an observer rated interview (INTERMED). Non-RTW was assessed two years after discharge from the rehabilitation. Discrimination was assessed by the area under the receiver operating curve (AUC) and calibration was evaluated with a calibration plot. The model was reduced with random forests. RESULTS: At 2 years, the non-RTW status was known for 2462 patients (77.5% of the total sample). The prevalence of non-RTW was 50%. The full model (36 items) and the reduced model (19 items) had acceptable discrimination performance (AUC 0.75, 95% CI 0.72 to 0.78 and 0.74, 95% CI 0.71 to 0.76, respectively) and good calibration. For the validation model, the discrimination performance was acceptable (AUC 0.73; 95% CI 0.70 to 0.77) and calibration was also adequate. CONCLUSIONS: Non-RTW may be predicted with a simple model constructed with variables independent of the patient's education and language fluency. This model is useful for all kinds of trauma in order to adjust for case mix and it is applicable to vulnerable populations like immigrant workers.", "link"=>"http://www.mendeley.com/research/predicting-non-return-work-after-orthopaedic-trauma-wallis-occupational-rehabilitation-risk-worrk-mo", "reader_count"=>31, "reader_count_by_academic_status"=>{"Librarian"=>1, "Researcher"=>2, "Student > Ph. D. Student"=>6, "Student > Postgraduate"=>4, "Student > Master"=>11, "Other"=>1, "Student > Bachelor"=>3, "Lecturer"=>3}, "reader_count_by_user_role"=>{"Librarian"=>1, "Researcher"=>2, "Student > Ph. D. Student"=>6, "Student > Postgraduate"=>4, "Student > Master"=>11, "Other"=>1, "Student > Bachelor"=>3, "Lecturer"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Nursing and Health Professions"=>3, "Medicine and Dentistry"=>15, "Agricultural and Biological Sciences"=>1, "Business, Management and Accounting"=>1, "Psychology"=>5, "Social Sciences"=>2, "Computer Science"=>3}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>15}, "Social Sciences"=>{"Social Sciences"=>2}, "Psychology"=>{"Psychology"=>5}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Computer Science"=>{"Computer Science"=>3}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>3}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"United Kingdom"=>1}, "group_count"=>3}

{"files"=>["https://ndownloader.figshare.com/files/1457226"], "description"=>"<p>Decision curve analysis of the Full Model (dashed line black line) and Reduced Model (blue solid line) in the development sample (Panel A) and the Reduced Model in the temporal validation sample (Panel B). The y-Axis represents the net benefit, which is the probability of true positives minus the probability of false-positives weighted for the threshold probability. With threshold probability (or risk thresholds) we mean the threshold above which a patient is declared at risk to not return to work at two years. The dashed red curve shows net benefit of considering all patients as positive (i.e. classified as being not returning to work). The benefit of considering all patients as returning to work was set as reference (solid grey horizontal line). In the left Panel (A) we see that the net benefits for both models are quite similar. The Full Modell would show advantages if a threshold would be set between 15% to 82%. The right Panel (B) shows that that the net benefit in the temporal validation sample is only little lower than in the development sample. Clear benefits are seen from risks thresholds from about 20 to 75%. The net benefit is calculated as (proportion of true positives) – (proportion of false positives)*pt/(1−pt), where pt is the threshold probability.</p>", "links"=>[], "tags"=>["health care", "Health care quality", "physiotherapy", "Socioeconomic aspects of health", "Public and occupational health", "Behavioral and social aspects of health", "Occupational and industrial medicine", "Sports and exercise medicine", "Surgical and invasive medical procedures", "Musculoskeletal system procedures", "Orthopedic surgery"], "article_id"=>993179, "categories"=>["Biological Sciences"], "users"=>["François Luthi", "Olivier Dériaz", "Philippe Vuistiner", "Cyrille Burrus", "Roger Hilfiker"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0094268.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Decision_curve_analysis_/993179", "title"=>"Decision curve analysis.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-09 03:21:41"}

{"files"=>["https://ndownloader.figshare.com/files/1457232"], "description"=>"<div><p>Background</p><p>Workers with persistent disabilities after orthopaedic trauma may need occupational rehabilitation. Despite various risk profiles for non-return-to-work (non-RTW), there is no available predictive model. Moreover, injured workers may have various origins (immigrant workers), which may either affect their return to work or their eligibility for research purposes. The aim of this study was to develop and validate a predictive model that estimates the likelihood of non-RTW after occupational rehabilitation using predictors which do not rely on the worker’s background.</p><p>Methods</p><p>Prospective cohort study (3177 participants, native (51%) and immigrant workers (49%)) with two samples: a) Development sample with patients from 2004 to 2007 with Full and Reduced Models, b) External validation of the Reduced Model with patients from 2008 to March 2010. We collected patients’ data and biopsychosocial complexity with an observer rated interview (INTERMED). Non-RTW was assessed two years after discharge from the rehabilitation. Discrimination was assessed by the area under the receiver operating curve (AUC) and calibration was evaluated with a calibration plot. The model was reduced with random forests.</p><p>Results</p><p>At 2 years, the non-RTW status was known for 2462 patients (77.5% of the total sample). The prevalence of non-RTW was 50%. The full model (36 items) and the reduced model (19 items) had acceptable discrimination performance (AUC 0.75, 95% CI 0.72 to 0.78 and 0.74, 95% CI 0.71 to 0.76, respectively) and good calibration. For the validation model, the discrimination performance was acceptable (AUC 0.73; 95% CI 0.70 to 0.77) and calibration was also adequate.</p><p>Conclusions</p><p>Non-RTW may be predicted with a simple model constructed with variables independent of the patient’s education and language fluency. This model is useful for all kinds of trauma in order to adjust for case mix and it is applicable to vulnerable populations like immigrant workers.</p></div>", "links"=>[], "tags"=>["health care", "Health care quality", "physiotherapy", "Socioeconomic aspects of health", "Public and occupational health", "Behavioral and social aspects of health", "Occupational and industrial medicine", "Sports and exercise medicine", "Surgical and invasive medical procedures", "Musculoskeletal system procedures", "Orthopedic surgery", "non", "orthopaedic", "wallis", "occupational"], "article_id"=>993185, "categories"=>["Biological Sciences"], "users"=>["François Luthi", "Olivier Dériaz", "Philippe Vuistiner", "Cyrille Burrus", "Roger Hilfiker"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0094268", "stats"=>{"downloads"=>17, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Predicting_Non_Return_to_Work_after_Orthopaedic_Trauma_The_Wallis_Occupational_Rehabilitation_RisK_WORRK_Model_/993185", "title"=>"Predicting Non Return to Work after Orthopaedic Trauma: The Wallis Occupational Rehabilitation RisK (WORRK) Model", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-04-09 03:21:41"}

{"files"=>["https://ndownloader.figshare.com/files/1457230"], "description"=>"<p>For the development sample only patients included with complete data on all variables and for the validation sample only patients with complete data on the variables from the final model are shown. AIS: Abbreviated Injury Scale.</p>", "links"=>[], "tags"=>["health care", "Health care quality", "physiotherapy", "Socioeconomic aspects of health", "Public and occupational health", "Behavioral and social aspects of health", "Occupational and industrial medicine", "Sports and exercise medicine", "Surgical and invasive medical procedures", "Musculoskeletal system procedures", "Orthopedic surgery", "validation"], "article_id"=>993183, "categories"=>["Biological Sciences"], "users"=>["François Luthi", "Olivier Dériaz", "Philippe Vuistiner", "Cyrille Burrus", "Roger Hilfiker"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0094268.t002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Characteristics_of_the_development_and_validation_study_population_overall_and_by_return_to_work_status_/993183", "title"=>"Characteristics of the development and validation study population overall and by return to work status.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-04-09 03:21:41"}